CHARM extracts ADPS priors from path-level radio maps to reduce 3D angle-delay-AoD search to 1D AoD search per path, delivering 34.8x speedup over joint OMP at T≤4 pilots with comparable accuracy and only 3.7 dB degradation under 0.2 rad dictionary mismatch via trust-region constraint.
Deep learning-based channel estimation for beamspace mmwave massive mimo systems
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A single recurrent transformer block trained once delivers 5 dB and 7.5 dB NMSE gains over prior methods for narrowband and wideband hybrid near-far field THz UM-MIMO channel estimation.
citing papers explorer
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Path-Level Radio Map-Aided Fast and Robust Channel Estimation for Pilot-Starved MIMO-OFDM Systems
CHARM extracts ADPS priors from path-level radio maps to reduce 3D angle-delay-AoD search to 1D AoD search per path, delivering 34.8x speedup over joint OMP at T≤4 pilots with comparable accuracy and only 3.7 dB degradation under 0.2 rad dictionary mismatch via trust-region constraint.
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Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO
A single recurrent transformer block trained once delivers 5 dB and 7.5 dB NMSE gains over prior methods for narrowband and wideband hybrid near-far field THz UM-MIMO channel estimation.